Ronaldo-GOAT/text_audio_pretraining_for_GAN

bw_jh_dataset — Robot 3D Point-Track Dataset (droid / hrdexdb / robocasa)

A unified multi-source robot manipulation dataset with 3D point tracks of the robot gripper and arm, calibrated multi-view RGB video, language instructions, and success labels. Three splits:

Split	Episodes	Size	Views	Source
droid	27,615 (+~1,050 in droid-11)	~280 GB	2 exterior ZED views	DROID (real, Franka)
hrdexdb	1,601	~998 GB	22 calibrated cameras	HRDexDB (real, xArm6 + dexterous hands)
robocasa	40,026	~273 GB	2 agentviews + ego wrist cam	RoboCasa (sim, Panda)

Getting the data

Each split ships as tar shards. Files larger than HF's 50 GB limit are binary-split into *.tar.partNN pieces — reassemble with:

cat hrdexdb-4.tar.part* > hrdexdb-4.tar   # parts are in order: part01, part02, ...
tar -xf hrdexdb-4.tar

Unsplit shards extract directly: tar -xf droid-01.tar. Every shard has a *.txt / *.manifest.txt file listing its episode ids.

Sample format (identical across splits)

Each episode is one directory named by its id:

<episode_id>/
  view1.mp4, view2.mp4, ...   # RGB videos (hrdexdb: up to view23; robocasa: also ego.mp4)
  gripper.npz                 # points: (T, 256, 3) float32 — 3D gripper point tracks
  robot.npz                   # points: (T, 512, 3) float32 — 3D robot arm point tracks
  object.npz                  # points: (T, 256, 3) float32 — 3D track of a manipulated object
  object2.npz, object3.npz…   # (robocasa multi-object tasks) one file per manipulated object
  meta.json                   # calibration + labels, see below

• All 3D points are in the view1 camera frame (OpenCV convention, meters).
• meta.json fields: views (per-view T_cam_world 4×4 with extrinsics_convention: "cam_from_world", and intrinsics {fx, fy, cx, cy, k1, k2, p1, p2, k3, width, height}), language_instructions, language_source, success, success_source, fps, n_frames, task_category, uuid.
• To project points into view v at frame t: p_world = inv(T_view1[t]) @ p_v1[t], then p_v = T_v[t] @ p_world, then apply view v's intrinsics (+ distortion).
• droid / hrdexdb: all cameras are a static rig, so T_view1 (and every T_v) is a single 4×4 and the per-frame index drops out.
• robocasa: view1/view2 ride the mobile base. Every meta carries a boolean camera_moves and max_base_displacement_m. For episodes where camera_moves is true, views.view1/view2 include T_cam_world_perframe (shape (T,4,4), cam_from_world per frame) — use it so world↔camera is consistent on every view while the base drives (the wrist ego view is natively per-frame). When camera_moves is false the single T_cam_world is exact at every frame. tools/visualize_episode.py uses the per-frame field automatically. See robocasa/base_motion_filter/base_motion_index.json for the full move classification (static / settle / moving) and per-episode t_rest.

Manipulated-object tracks (robocasa)

Each robocasa episode ships one object*.npz per manipulated object — the object(s) the robot actually picks/moves. These are selected deterministically from the simulator state: an object is included iff its body moves more than 5 cm during the episode. Receptacles you place into (pans, tupperware, plates) and distractors stay static and are excluded. Multi-object tasks (e.g. PreSoakPan, SearingMeat, PackIdenticalLunches) get object.npz, object2.npz, … one per moved object; meta.json → object lists them (cfg, category, file, max_disp_m). Fixture/turn tasks (open/close, faucet, heat) manipulate no free object and ship none.

DROID split contents

The droid shards here (droid-XX-success-lang.tar) contain only success episodes with language instructions (27,615 episodes = success ∩ language-annotated, drawn from our calibration-filtered export list), and their meta.json files are already patched with the verified instructions (merged from the official aggregated annotations and the KarlP/droid 75k release) — no post-processing needed. Each shard has a matching droid-XX-success-lang.txt episode-id list. droid-11-success-lang.tar adds the same filtering over episodes exported after the main shards.

For reference/audit, droid/droid_language_recovered.json documents the full language-recovery provenance (per-entry source, donor, n_donors, donor_agreement) — see droid_language_recovered_README.md. droid-meta-patch-success.tar is the standalone metadata patch used to produce these shards.

Base-motion filter (robocasa)

robocasa's view1/view2 cameras are mounted on the robot's mobile base, and each episode stores a single frame-0 extrinsic — stale for frames after the base drives. robocasa/base_motion_filter/ classifies every episode from its per-frame base pose (source parquets, validated against sim to 0.1 mm):

class	episodes	meaning
static	31,193 (54.7%)	base never moves (>1 cm effective) — use as-is
settle	24,167 (42.4%)	base moves then rests ≥30 frames — trim to the static tail (per-episode t_rest in the .settle.trim lists / base_motion_index.json)
moving	1,679 (2.9%)	base still moving at the end — drop

tools/robocasa_base_filter.py applies this: keeps static episodes unchanged and trims settle episodes (videos cut, npz sliced — points are stored in the per-frame view1 camera frame so no re-projection is needed — and view1/view2 T_cam_world analytically corrected to the rest pose; validated to 0.000 mm vs simulator ground truth). 97.1% of episodes remain usable.

Visualizer

tools/visualize_episode.py renders a row-concatenated multi-view video with the 3D point tracks re-projected onto every view using only the stored calibration — use it to verify calibration/npz consistency:

python tools/visualize_episode.py --split droid --episode 0 --root <extracted_root>
python tools/visualize_episode.py --split hrdexdb --episode allegro_v5__apple__3 --root <extracted_root>

--episode accepts an integer index or an episode id. Dependencies: numpy, opencv-python. Example outputs are in tools/examples/.

Verification pairs

droid/debug_lang_pairs/ contains side-by-side fail-vs-success videos demonstrating the failure-instruction borrowing (failure left/red, same-session success donor right/green) with pair_info.json provenance for each.

Provenance & licenses

• droid: derived from DROID (CC-BY-4.0); language annotations merged from the official aggregated annotations and the KarlP/droid 75k release.
• robocasa: derived from RoboCasa datasets.
• hrdexdb: HRDexDB multi-camera teleoperation recordings.
• Success labels come from the source datasets' own labels/path conventions; 8 label-contradictory droid episodes were excluded entirely.